Imbalanced gut microbiota fuels hepatocellular carcinoma development by shaping the hepatic inflammatory microenvironment

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, and therapeutic options for advanced HCC are limited. Here, we observe that intestinal dysbiosis affects antitumor immune surveillance and drives liver disease progression towards cancer. Dysbiotic microbiota, as seen in Nlrp6−/− mice, induces a Toll-like receptor 4 dependent expansion of hepatic monocytic myeloid-derived suppressor cells (mMDSC) and suppression of T-cell abundance. This phenotype is transmissible via fecal microbiota transfer and reversible upon antibiotic treatment, pointing to the high plasticity of the tumor microenvironment. While loss of Akkermansia muciniphila correlates with mMDSC abundance, its reintroduction restores intestinal barrier function and strongly reduces liver inflammation and fibrosis. Cirrhosis patients display increased bacterial abundance in hepatic tissue, which induces pronounced transcriptional changes, including activation of fibro-inflammatory pathways as well as circuits mediating cancer immunosuppression. This study demonstrates that gut microbiota closely shapes the hepatic inflammatory microenvironment opening approaches for cancer prevention and therapy.

H epatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide and the dominant cause of death in patients with compensated liver cirrhosis 1,2 . HCC incidence keeps rising and despite recent advances, therapeutic options remain limited 3 . HCC frequently arises in the context of chronic liver diseases (CLDs), with viral hepatitis B and C as well as alcoholic and non-alcoholic steatohepatitis (NASH) being the most common causes 4 . These conditions are characterized by chronic hepatic inflammation and continuous liver damage leading to hepatocyte cell death, which prompts compensatory proliferation and precedes hepatocarcinogenesis 5 . Therefore, a better understanding of these inflammatory processes is critical for developing new therapeutic strategies.
The NF-kB pathway is a core-signaling hub in hepatocytes that integrates the activity of various stress-related and inflammatory mediators 6 . NF-kB is a transcription factor that can translocate to the nucleus and initiate gene transcription. Two different pathways can trigger its activation: the canonical pathway via cytokines such as TNFα, IL-1β, or TLR agonists and the noncanonical pathway, which is mainly important in B cells 6 . The canonical pathway is mediated by phosphorylation of IκB by a highmolecular kinase complex, which is formed by two different catalytic IκB kinase 1 (IKK1 or alpha) and IKK2 (also IKKbeta) subunits as well as its regulatory subunit IκB kinase (IKK) subunit NF-kB essential modulator (NEMO or IKKγ). We and others have shown that blocking NF-kB activity in hepatocytes reduces inflammatory gene expression 7 . However, this process at the same time results in impaired expression of anti-apoptotic genes promoting overshooting cell death and compensatory proliferation 8 . Hence, conditional ablation of NEMO triggers spontaneous steatohepatitis and hepatocarcinogenesis in 12-months-old NEMO Δhepa mice 8,9 . These mice are a well-established model to study steatohepatitis progression towards HCC 8 . In previous work, we have shown that activation of the canonical NF-kB pathway by experimental stimuli such as lipopolysaccharide (LPS) exacerbates this phenotype 10 .
The liver receives 2/3 of its blood supply through the portal vein. Hence, the liver is continuously exposed to a vast amount of pathogen-and microbe-associated molecular patterns (PAMPs and MAMPs), which bind to pathogen recognition receptors (PRRs), trigger NF-kB activation in hepatocytes and nonparenchymal cells, thereby driving liver disease progression 11 . This may critically shape the hepatic inflammatory microenvironment and fuel HCC development in the absence of NEMO.
In the diseased liver, senescence surveillance of pre-malignant hepatocytes by T cells limits liver cancer development by mounting specific immune responses 12 . Based on their high expression of PRRs, Ly6C hi CD11b + F4/80 low monocytic-derived suppressor cells (MDSCs) are well equipped to sense MAMPs and expand upon PRR activation 13 . Importantly, these cells can suppress the CD8 + cytotoxic T cell response and thus limit antitumor immunity 14 .
Mice lacking the inflammasome sensor molecule NLRP6 develop a dysbiotic colitogenic microbiota composition when housed under specific pathogen-free(SPF) conditions 15,16 . It has been shown that intestinal dysbiosis in mice deficient for NLRP6 promotes steatohepatitis via Toll-like receptor 4 (TLR4) and TLR9, a phenotype transmissible to co-housed wild-type (WT) mice 17 . Conversely, we and others have shown that microbiota depletion using broad-spectrum antibiotics dampens experimental steatohepatitis 17,18 . In recent clinical landmark studies, the presence of the gut microbiota and distinct bacteria were essential for an efficient anti-tumor immunotherapy [19][20][21] . Studies have linked the presence of the bacterium Akkermansia muciniphila to favorable treatment response to immunotherapy in several solid malignancies, including HCC 20,21 . Interestingly, murine studies show that oral supplementation with A. muciniphila reduces pro-inflammatory bacterial (LPS) and improves alcoholic liver disease 22,23 .
While the link between intestinal dysbiosis and liver disease progression is well established, mechanisms by which gut microbiota and bacterial translocation shape the hepatic inflammatory milieu and affect anti-tumor immune surveillance remain incompletely understood 24 . In human liver disease, intestinal dysbiosis is associated with intestinal barrier impairment, reduced microbiota diversity, overgrowth of certain unfavorable bacteria, and absence of beneficial communities 25 . Since Nlrp6 −/− mice mimic these hallmarks of intestinal dysbiosis, we used these mice as a tool to investigate how intestinal dysbiosis orchestrates the tumor microenvironment and affects the anti-tumor response during steatohepatitis progression 26 . We hypothesized that Nlrp6 −/− -mediated intestinal dysbiosis aggravates steatohepatitis and increases tumor burden in NEMO Δhepa mice. In this work, we demonstrate that intestinal barrier impairment and bacterial translocation dynamically induce expansion of mMDSCs and suppression of CD8 + T cells, which can be blocked by antibiotic treatment and reversed by the targeted reintroduction of the commensal A. muciniphila. Similarly, in a cohort of cirrhosis patients, we observe a strong association between bacterial translocation and activation of fibro-inflammatory pathways that mediate cancer immunosuppression highlighting the close functional interaction between gut and liver during liver disease progression.
As previously shown, in the absence of NEMO, activation of the canonical NF-kB pathway results in hepatocyte cell death due to the loss of anti-apoptotic gene expression 8 . Therefore, we characterized how hepatic inflammation impacts cell death and compensatory proliferation in these mice. While mRNA expression of the pro-inflammatory cytokines Tnfa, Il6, Il1β as well as inflammasome components Caspase-1 and Nlrp3 remained unchanged in tumor tissue ( Supplementary Fig. 1d), infiltration of myeloid CD11b + was linked to higher gene expression levels of the pro-inflammatory genes Tnfa, Tlr4, Il1β, Nlrp3 and Ccl5 in whole liver tissue without macroscopic tumors (Fig. 1g, Supplementary Fig. 1e). Previous reports suggest that absence of NLRP6 can result in overactivation of NLRP3 27 . Whereas Nlrp3 expression was increased on the mRNA level, we could not confirm this on the protein level ( Supplementary Fig. 1e, f). Moreover, Nlrp3 gene expression was unchanged in the early 13 weeks' time point, suggesting that overexpression of NLRP3 inflammasome in the absence of NLRP6 does not mediate the observed phenotype ( Supplementary Fig. 1g). The inflammatory phenotype of 52-week-old mice was associated with pronounced pJNK activation in NEMO Δhepa /Nlrp6 −/− mice (Fig. 1h) correlating with increased apoptotic hepatocyte cell death shown by cleaved Caspase3 staining and compensatory proliferation, based on KI67 staining (Fig. 1i, j and Supplementary Fig. 1h).
Together, these data demonstrate that the absence of NLRP6 orchestrates the inflammatory response in the tumor microenvironment and drives liver disease progression towards fibrosis and cancer in NEMO Δhepa /Nlrp6 −/− mice.
Interestingly, although NEMO Δhepa and NEMO Δhepa /Nlrp6 −/− were co-housed with their respective NEMO fl/fl (referred to as WT) and NEMO fl/fl /Nlrp6 −/− littermate controls, conditional NEMO deficiency and the resulting steatohepatitis had a reproducible impact on microbiota composition. Importantly, beta-diversity analysis confirmed the development of a distinct microbiota in the Nlrp6 −/− line, which explained the highest proportion of 16% of total microbiota variability (Fig. 2a).
We performed differential abundance analysis based on the negative binomial distribution (DESeq2) and linear discriminant effect size analysis (LEfSe) to dissect which specific bacteria account for the observed differences. These analyses revealed a strong relative increase in Muribaculum in NEMO Δhepa /Nlrp6 −/− mice, while Verrucomicrobiaceae and several members of the family Lachnospiraceae were significantly reduced (Fig. 2b, c). Pairwise comparisons of all groups revealed a decrease of Roseburia and Lachnospiraceae in Nlrp6 −/− mice compared to WT mice. Interestingly, Akkermansia were increased in NEMO Δhepa compared to WT littermates, which disappeared in NEMO Δhepa / Nlrp6 −/− (Supplementary Fig. 2a-c).
Next, we analyzed intestinal tissue sections of the different genotypes to explore the functional implications of the observed changes in microbiota composition. A. muciniphila is a wellknown mucin-degrading bacterium. Its presence has been linked to thickening of mucus layers and intestinal barrier improvement 28 . Therefore, we analyzed colonic mucus layers. Loss of NLRP6 was associated with reduced thickness of colonic mucus layers, which was most pronounced in NEMO Δhepa / Nlrp6 −/− mice ( Supplementary Fig. 2g).
Together, intestinal dysbiosis upon lack of NLRP6 expression prompted disruption of the intestinal TJ barrier and increased inflammatory gene expression.
Intestinal permeability correlates with steatohepatitis activity and increased tumor burden. Next, we studied the functional implications of intestinal dysbiosis and intestinal barrier impairment in NEMO Δhepa /Nlrp6 −/− mice. Here, we evaluated in vivo intestinal barrier function in a cohort of 52-week-old NEMO Δhepa and NEMO Δhepa /Nlrp6 −/− mice and respective controls. Permeability of the intestinal barrier measured by 4000 kDa FITCdextran was significantly increased in NEMO Δhepa /Nlrp6 −/− compared to NEMO Δhepa controls (Fig. 2h). Strikingly, ALT levels and tumor number demonstrated a strong correlation with intestinal permeability (Fig. 2i, Supplementary Fig. 2j), indicating that intestinal barrier impairment exacerbates liver disease in NEMO Δhepa mice.
Loss of NLRP6 shapes the hepatic immune environment. Next, we addressed how NLRP6 orchestrates the hepatic immune environment at an early stage of cancer development. 13-week-old NEMO Δhepa /Nlrp6 −/− mice displayed increased leukocyte infiltration, aggravated liver fibrosis evidenced by SR staining and increased hepatocyte proliferation supported by immunohistochemistry (IHC) staining for Ki67 (Fig. 3a, b, Supplementary  Fig. 3a). More pronounced liver injury was reflected by significantly increased ALT, AST and GLDH levels in NEMO Δhepa / Nlrp6 −/− mice compared to NEMO Δhepa mice (Fig. 3c, Supplementary Fig. 3c). In line, NEMO Δhepa /Nlrp6 −/− mice displayed a significantly increased liver-to-body weight ratio compared to NEMO Δhepa mice ( Supplementary  Fig.  3d). Epithelial-mesenchymal transition during HCC development can be triggered by stellate cells. Indeed, we found increased stellate cell activation evidenced by aSMA staining in 13-week-old NEMO Δhepa /Nlrp6 −/− compared to NEMO Δhepa mice ( Supplementary  Fig. 3e).
NEMO Δhepa /Nlrp6 −/− mMDSCs suppress T cell proliferation in vitro. MDSCs cannot be sufficiently defined based on cell surface marker expression 30 . Thus, we performed additional stainings and functional assays to further study the phenotype of these cells. CD11b + mMDSCs and CD8 + T cells showed close proximity, which was most pronounced in livers of NEMO Δhepa / Nlrp6 −/− mice ( Supplementary Fig. 5a). Next, we isolated and further characterized these cells and explored their inhibitory capacity on T cells by performing in vitro assays ( Supplementary  Fig. 5b). T cells were isolated from WT and Nlrp6 −/− spleens, labeled with CFSE, stimulated with CD3/CD28, and co-cultured with granulocytic MDSC (defined as CD45 + Ly6G + Gr1 hi ) or mMDSCs (defined as CD45 + Ly6G − Gr1 hi ) isolated from NEMO Δhepa or NEMO Δhepa /Nlrp6 −/− livers by magneticactivated cell sorting (MACS). We did not observe baseline differences in T cell proliferation between WT and Nlrp6 −/− mice ( Supplementary Fig. 5c, d). Moreover, T cell proliferation remained unaffected upon co-culture with hepatic gMDSCs from either genotype ( Supplementary Fig. 5c, d). However, mMDSCs strongly suppressed CD8 + T cell proliferation, most pronounced upon co-culture with mMDSCs isolated from NEMO Δhepa / Nlrp6 −/− livers (Fig. 3j).
Together, these data demonstrate that intestinal dysbiosis in NEMO Δhepa /Nlrp6 −/− mice is associated with an expansion of mMDSCs, which had a stronger suppressive capacity when isolated from NEMO Δhepa /Nlrp6 −/− than from NEMO Δhepa mice.
Microbiota depletion reshapes hepatic inflammatory microenvironment and ameliorates steatohepatitis. To test whether microbiota shapes the hepatic inflammatory response in NEMO Δhepa /Nlrp6 −/− livers, we treated 8-week-old mice until week 13 using an established combination of non-absorbable broad-spectrum antibiotics (ABx). As previously described 31 , this treatment led to an almost complete microbiota depletion evidenced by enlarged caeca similar to germ-free mice as well as significantly reduced total bacterial DNA content in fecal samples evidenced by qPCR assays using primers for all bacteria (Supplementary Fig. 6a). Strikingly, after ABx treatment liver transaminase levels of NEMO Δhepa /Nlrp6 −/− mice were similar to NEMO Δhepa mice and significantly reduced compared to nontreated mice (Fig. 4a). Importantly, ABx treatment resulted in a significant reduction of mMDSC as evidenced by FACS and also reflected in a lower number of CD11b + cells in IF staining (Fig. 4b, c). Conversely, ABx treatment resulted in an expansion of CD8 + T cells and CD4 + T cells, which almost reached the level as found in NEMO Δhepa livers (Fig. 4b, Supplementary Fig. 6b).
To further test the pathogenic relevance of Nlrp6 −/− microbiota, we performed fecal microbiota transfer (FMT) of NEMO Δhepa / Nlrp6 −/− mice into NEMO Δhepa mice. In accordance with successful FMT, NEMO Δhepa receiving NEMO Δhepa /Nlrp6 −/− microbiota formed a distinct cluster that clustered close to  Fig. 6c). Microbiota transfer resulted in a shift of NEMO Δhepa microbiota along NMDS axis 1. DESeq2 as well as LEfSe analyses comparing NEMO Δhepa mice with and without FMT revealed that this shift was mainly driven by differential abundance of A. muciniphila (Fig. 4d, Supplementary Fig. 6d). Strikingly, upon FMT all recipient NEMO Δhepa mice were lacking A. muciniphila ( Supplementary Fig. 6e), and only one differential OTU (OTU23_ambiguous_taxa) was observed in LEfSe analyses comparing NEMO Δhepa FMT-recipient with NEMO Δhepa /Nlrp6 −/− FMT-Donors, further supporting successful microbiota transfer.
FMT resulted in a significant increase in liver transaminases AST and ALT in NEMO Δhepa animals (Fig. 4e). Interestingly, FMT of NEMO Δhepa /Nlrp6 −/− microbiota prompted an increase in absolute numbers of CD45 + hepatic leukocytes, significant expansion of hepatic mMDSCs and suppression of cytotoxic T cells in recipient NEMO Δhepa mice ( Fig. 4f-h, Supplementary  Fig. 6f).
Consistent with the involvement of TLR4 in mMDSC expansion, both 8-and 52-week-old NEMO Δhepa /Tlr4 −/− displayed a reduced abundance of these cells ( Supplementary  Fig. 6g). This observation was associated with significantly reduced liver transaminase levels ( Supplementary Fig. 6h, i), reduced cell death as well as proliferation, and a markedly reduced tumor burden in 52-week-old mice (Supplementary  Fig. 6j-l). Interestingly, reduced mMDSC abundance in NEMO Δhepa /Tlr4 −/− mice was associated with an increase in CD3 + CD4 + T cells ( Supplementary Fig. 6m).
To determine whether this immunologic phenotype was mediated by hematopoietic or parenchymal cells, we performed bone marrow transplantation experiments. Bone marrow chimeric NEMO Δhepa mice receiving Tlr4 −/− donor bone marrow demonstrated a significant >6-fold reduction in mMDSC abundance compared to NEMO Δhepa mice receiving WT control bone marrow pointing towards a role of TLR4 in hematopoietic cells for the observed phenotype ( Supplementary Fig. 6n).
Together, these data demonstrate that the immune phenotype of NEMO Δhepa /Nlrp6 −/− mice is transmissible to NEMO Δhepa mice upon FMT. Precisely, TLR4 signaling in hematopoietic cells augments mMDSC infiltration and promotes steatohepatitis progression towards HCC.
Specific alterations of gut microbiota correlate with liver disease phenotype in NEMO Δhepa mice. Intestinal microbiota of NEMO Δhepa /Nlrp6 −/− was significantly different from NEMO Δhepa mice after 13 and 52 weeks. Interestingly, microbiota modulation immediately reshaped the hepatic inflammatory microenvironment. In a final experiment, we, therefore, aimed to explore which specific changes in microbiota may modulate liver disease activity in NEMO Δhepa mice. LEfSe analysis showed a major relative reduction in A. muciniphila in 13-and 52-week-old NEMO Δhepa /Nlrp6 −/− compared to NEMO Δhepa mice. The bacterium A. muciniphila was significantly reduced in 13-and 52week-old NEMO Δhepa /Nlrp6 −/− as well as NEMO Δhepa mice receiving FMT (Fig. 2b, Supplementary Fig. 6d). Moreover, in NEMO Δhepa microbiota relative abundance of A. muciniphila inversely correlated with hepatic mMDSC abundance (Spearman-r = 0.8508, p = 0.0005) as well as serum ALT and GLDH levels highlighting the relevance of these bacteria in mediating the observed phenotype (Fig. 5a, Supplementary Fig. 7a). Hence, we tested our hypothesis that the transfer of A. muciniphila ameliorates liver disease in NEMO Δhepa mice. 8-week-old NEMO Δhepa littermate mice were gavaged orally with 2*10 8 colony forming units (CFUs) A. muciniphila or anaerobic PBS 3-times a week for 5 weeks (Fig. 5b). Successful microbiota transfer was confirmed by RT-qPCR and 16S rRNA gene amplicon sequencing of stool samples before and after 5 weeks of gavage (Fig. 5c, Supplementary Fig. 7b, c). Interestingly, AKK treatment did not only increase the abundance of this specific bacterium but resulted in a significant shift in microbiota composition as reflected in distinct clustering in principal coordinates analysis (PCoA) (Fig. 5c,  Supplementary Fig. 7d). Treatment with A. muciniphila explained a large proportion of total microbiota variability observed in these mice (R 2 = 0.403, *p < 0.05).
LEfSe analysis revealed a reduction of the phylum Bacteroides and expansion of Akkermansia (Fig. 5d). In DESeq2 analyses, Akkermansia supplementation also led to an increase in the abundance of Lachospiraceae and Blautia, which was associated with an increase in overall microbiota richness ( Supplementary  Fig. 7e). Increased abundance of the genus A. muciniphila in NEMO Δhepa mice after transfer could also be confirmed using AldeX2 (Supplementary Table 1). In line with these data, we observed a significant expansion of the colonic mucus layers and increased ZO-1 expression in NEMO Δhepa mice gavaged with A. muciniphila compared to PBS treated control mice (Fig. 5e, f,  Supplementary Fig. 7f).
Together these data demonstrate that continuous A. muciniphila supplementation can reduce liver injury, inflammation, and fibrosis even in the presence of host-derived factors that promote dysbiosis such as NLRP6 deficiency.
Bacterial translocation is higher in cirrhosis patients and shapes the hepatic transcriptomic landscape. We next tested if our observation in mice may also applies to humans. We therefore collected snap frozen liver tissue from patients with advanced liver cirrhosis of mixed etiology that underwent liver transplantation (n = 43) and controls undergoing other abdominal surgery (n = 12) (Supplementary Table 2). Small specimen from the same tissue region were cut and further processed for mRNA and isolation of bacterial DNA. Next, we analyzed hepatic bacterial DNA abundance using 16S rRNA gene amplicon sequencing in a strictly controlled environment using a stringent contaminationaware approach described and discussed previously 32,33 .
We also studied host gene expression by mRNA sequencing to assess how bacterial translocation affects the hepatic transcriptomic landscape (study outline see Fig. 6a).
Cirrhosis patients displayed significantly higher 16S rRNA gene copies per ng of total DNA compared to controls measured by RT-qPCR (Fig. 6b). Cirrhosis patients displayed higher alphadiversity reflected in the Shannon index ( Supplementary Fig. 9a). MDS ordination showed moderate clustering between the cirrhosis and control group ( Supplementary Fig. 9b) (R 2 = 0.038, p = 0.01, ADONIS) and Stenotrophomonas, Roseburia, Sphingobiom as well as Psychrobacter discriminated cirrhosis patients from controls in LEfSe analyses ( Supplementary  Fig. 9c). The order Lactobacillales was negatively correlated with patient's MELD score and bilirubin levels (Supplementary Table 3).
Bacterial translocation shapes the inflammatory microenvironment and promotes expression of T cell exhaustion markers in liver cirrhosis. Based on our murine data, we next specifically investigated the impact of bacterial translocation on the hepatic inflammatory milieu in human liver cirrhosis. To this end, we computationally dissected the hepatic cellular landscape based on gene expression profiles using xCell 36 . Cirrhotic livers showed enrichment for overall immune and stroma cells reflected in the microenvironment, stroma, and Immune xcell-scores, while the hepatocyte score was found to be relatively reduced (Fig. 6d). Interestingly, higher 16S rRNA gene abundance was associated with increased CD8 + T cells, NKT cells, central memory CD4 + and regulatory T cells, the latter of which have been implicated in tumor immunosuppression (Fig. 6d, Supplementary Table 5).
Activation of an innate immune response upon exposure with MAMPs and PAMPs depends on sensing via PRRs. Interestingly, the expression of several PRRs such as NOD1, NLRP3, and TLR2 all correlated with bacterial translocation (Supplementary Fig. 9h). MDSCs are important sensors of MAMPs and PAMPs and may dampen T cell function. Expression levels of the MDSC markers colony-stimulating factor receptor2a (CSFR2A) and interleukin1-receptor2 (IL1R2) both demonstrated a strong correlation with 16S rRNA gene abundance ( Supplementary Fig. 9i).
Together, these data show that bacterial translocation in cirrhotic patients is strongly associated with fibro-inflammatory pathways as well as TF activation linked to immunosuppression and T cell exhaustion.

Discussion
In the last decade, the gut-liver axis and gut microbiota have emerged as cornerstones in the pathogenesis of chronic liver diseases 11,[42][43][44] . Our present study defines intestinal barrier impairment and bacterial translocation as key mechanisms that shape the hepatic inflammatory microenvironment and fuel liver disease progression towards cirrhosis and HCC.
Chronic diseases, environmental and dietary factors associated with modern western lifestyles as well as medication have been found to contribute to intestinal dysbiosis. These factors trigger qualitative and quantitative changes in bacterial communities and directly affect the systemic inflammatory status 25,45 . As the liver is constantly exposed to a vast amount of microbiota-derived products from the gut via the portal vein, changes in intestinal homeostasis particularly impact liver physiology 11,46 .
Mouse models represent a suitable tool to functionally study mechanisms of gut-liver interaction allowing comprehensive mechanistic investigations. For our mechanistic studies, we decided to study chronic liver disease progression in NEMO Δhepa mice lacking the inflammasome sensor molecule NLRP6, which has been identified as an important regulator of host-microbial crosstalk at the gut mucosal surface 15 . NEMO Δhepa mice develop spontaneous steatohepatitis 8 , liver fibrosis and finally HCC. Interestingly, microbiota of NEMO Δhepa was different from WT mice and these mice demonstrated reduced barrier function compared to WT littermate controls. The mechanisms by which hepatic loss of NEMO impairs in the intestinal barrier will be subject to future studies and could be mediated by changes in bile acid composition or low-grade systemic inflammation. Although the loss of NLRP6 NEMO other IKK components have not been described in human HCC, this mouse model nicely reflects essential mechanisms of human liver disease progression and allowed us to study the impact of intestinal dysbiosis induced by loss of NLRP6 on liver disease progression. In our study, changes in gut microbiota composition of NEMO Δhepa /Nlrp6 −/− mice translated into impaired intestinal barrier function strongly correlating with markers of steatohepatitis activity as well as tumor burden. Interestingly, microbiota of NEMO Δhepa /Nlrp6 −/− mice was significantly different from NEMO Δhepa mice. Specifically, Nlrp6 deletion was associated with an increased abundance of the pathobiont Muribaculum, while Akkermansia muciniphila was absent. Several human, as well as murine studies, have demonstrated health benefits of A. muciniphila by promoting intestinal barrier function via regulation of intestinal mucus layers and acetate and propionate production 22,23,28,47 . Interestingly, loss of A. muciniphila has recently been described in patients with early HCC 48 .
To address whether the observed phenotype was caused by altered microbiota, we performed microbiota modulation experiments. Interestingly, the phenotype of NEMO Δhepa /Nlrp6 −/− mice was transmissible via FMT of the unfavorable NEMO Δhepa /Nlrp6 −/− community and reversible upon ABx treatment. NLRP6 deficiency in NEMO Δhepa mice or transfer of NEMO Δhepa /Nlrp6 −/− microbiota transfer into NEMO Δhepa mice triggered a pronounced infiltration of hepatic myeloid cells (defined as CD11b + Ly6G + Gr1 hi ). We termed these cells as mMDSCs based on surface marker expression and after confirming their in vitro suppressive capacity on T cell proliferation. These dynamic changes were linked to the reduced abundance of T cells pointing towards the high cellular plasticity of the hepatic inflammatory microenvironment related to microbiota. Several studies have highlighted the anti-tumor activity of CD8 + T cells in HCC 49,50 , however, this is dependent on their phenotype and tissue microenvironment 51 . They may also promote liver damage and progression towards HCC 52 . Future CD8 + T cell depletion experiments could help to establish causality in this model. HCC development Kupffer cells and macrophages may undergo phenotypic changes and promote a pro-tumorigenic microenvironment 53 . In our study we did not observe changes in Kupffer cell abundance, however, hepatic gene expression pointed towards an M2-skewed microenvironment. Regulatory T cells can be programmed in the gut and might exert their immunosuppressive function in the liver 54,55 . While not being the focus of our study, future studies on this mechanism might advance our understanding of gut-mediated immune modulation during HCC development.
Based on previous data and the landmark paper by Dapito et al., we hypothesized that PRR signaling and especially TLR4 may be an important orchestrator of this response 56 . Interestingly, NEMO Δhepa /Tlr4 −/− mice displayed a reduced abundance of MDSCs and an increase in CD4 + T cells, which was linked to a lower tumor burden at 52 weeks. Accordingly, transfer of the dysbiotic NEMO Δhepa /Nlrp6 −/− community failed to induce an expansion of mMDSCs in NEMO Δhepa /Tlr4 −/− mice. In line with previous data, these results clearly suggest an involvement of TLR4 mediated PRR signaling in MDSC expansion 57 . However, we cannot exclude that other PRRs, as well as other microbiota dependent pathways, may form equally important circuits directing the inflammatory response in the cirrhotic liver 58 .
Almost all cases of HCC arise in the context of cirrhosis, where chronic inflammation mediated by innate and adaptive immune responses drives disease progression 59 . However, immunosurveillance by T and B cells can also limit hepatocarcinogenesis 12 . In this context, the interplay between T cells and MDSCs is critical as MDSCs accumulation may induce T cell exhaustion promoting HCC progression 60,61 . Hence, immune-mediated mechanisms are essential during liver disease progression towards malignant transformation and dissecting their different functions will define novel therapeutic options. Gut microbiota can direct hepatic immunity in multiple ways via MAMPs, microbial metabolites, bile acids as well as short chain fatty acids 62,63 .
In patients, assessing the intestinal barrier as well as bacterial translocation and its molecular impact on the liver are challenging as there are no good non-invasive serum markers of barrier dysfunction and hepatic inflammation. While a series of studies have linked altered gut microbiota composition and metagenomic profiling to clinical and histopathological cirrhosis phenotypes 64,65 , data on whether these changes direct the hepatic inflammatory response or modulate the transcriptional landscape have not been available yet 24 . Based on our murine models, we hypothesized that bacterial translocation may also shape the hepatic inflammatory microenvironment in patients with advanced liver cirrhosis. We, therefore, assessed bacterial translocation by an established protocol of 16S rRNA analysis from liver tissue, quantified total bacterial DNA content, performed 16S rRNA gene amplicon sequencing and correlated these data with transcriptomic data generated from the same tissue specimen. In line with our murine data, bacterial translocation strongly correlated with fibro-inflammatory transcriptional pathways in human liver cirrhosis implicating bacterial translocation as a driver of liver disease progression.
A recent clinical study compared 20 Child Pugh A cirrhotic NAFLD patients with and without early HCC 48 . The authors found increased serum markers of intestinal inflammation (calprotectin) as well as permeability (ZO-1 and LPS) in cirrhosis patients vs. healthy controls. Interestingly, impaired barrier function was associated with reduced abundance of Akkermansia in NAFLD cirrhotic patients compared with controls and correlated with circulating mMDSCs in the HCC group.
Our functional data in the NEMO Δhepa mouse model are in line with these findings. Moreover, in NEMO Δhepa mice we observed profound changes of mMDSC and T cell abundance after short- Fig. 6 Hepatic bacterial 16s rDNA is increased in cirrhosis patients and shapes the hepatic transcriptomic landscape. a Study outline: Snap frozen surgical liver tissue specimen were taken from 44 cirrhosis patients that underwent liver transplantation or 11 healthy controls. DNA and mRNA were isolated from the same tissue specimen and tissue region and subjected to 16 s rRNA gene amplicon sequencing or mRNA sequencing. b 16s rRNA gene copies/ng DNA determined by real time quantitative PCR in control (n = 12) and cirrhotic (n = 43) liver (Mann-Whitney-U-Test, p = 0.014). c Pathway activity based on mRNA-sequencing data and inferred by PROGENy computational pathway analysis in cirrhosis patients (n = 22) vs. healthy controls (n = 8). Correlation of 16S rRNA gene abundance with pathway activation (Spearman correlation, n = 30 pairs). d Computational Cell type enrichment analysis and correlation of calculated cell types with 16S rRNA gene abundance of cirrhotic patients (n = 22) and healthy controls (n = 8). e 16S rRNA gene abundance strongly correlates with the expression of immune checkpoint genes (Spearman correlation, n = 30 pairs, two-tailed). f Correlation of CTLA4 and g transcription factors involved in T-cell exhaustion (TOX, IRF4) with rRNA gene copies/ng genomic DNA (Spearman, n = 30 pairs, all 2-tailed, p < 0.0001). All Data are presented as the mean ± standard error of the mean (SEM) and considered significant at p < 0.05 (*), p < 0.01 (**), p < 0.001 (***). Source data are provided as a Source data file. term microbiota modulation. Additionally, supplementation with the single bacterium Akkermansia muciniphila improved intestinal barrier function, reduced infiltration of MDSCs and dampened steatohepatitis activity. Together, these data call for further studies to assess therapeutic supplementation with Akkermansia in HCC patients. The ideal study would involve the collection of liver biopsies as well as microbiota specimens, which would allow correlation of intestinal microbiota with hepatic 16S rRNA gene abundance and the hepatic transcriptional profile.
In a recent clinical trial daily supplementation of A. muciniphila for 3 months was well-tolerated, improved insulin sensitivity and blood lipid profiles in obese insulin-resistant individuals 66 . Similar to our murine data, modulating gut microbiota has the potential to reshape the hepatic inflammatory milieu in HCC patients, a hypothesis that is also inspired by a series of studies highlighting the role of microbiota in immune checkpoint therapy 20,67,68 . Here, recent studies have linked Akkermansia abundance to favorable treatment responses, while broad spectrum antibiotic intake before therapy which induces intestinal dysbiosisimpaired treatment responses 20 . While the strong immune-mediated pathogenesis highlights HCC as a particularly interesting target for immunotherapies, characteristics of the hepatic tumor microenvironment define a high barrier of resistance to immunotherapy 69,70 . Although the cirrhotic liver tissue they studied may not specifically reflect the HCC microenvironment, the observed correlations between hepatic 16S rRNA abundance and expression of fibro-inflammatory pathways, genes involved in cancer immunosuppression as well as MDSCs, T cell exhaustion, and PRR-signaling are likely also relevant in disease progression towards HCC. It is tempting to speculate that hepatic 16S rRNA gene abundance may serve as a biomarker of intestinal barrier impairment and dysbiosis that helps to predict treatment response to immune therapies and identify patients that could benefit from microbiota modulation. PCR-based measurements could be easily implemented in standard clinical biopsy workflows. A limitation of our study is that functional microbiota modulation studies were only performed in mice-additional studies in humans are needed. Based on our microbiota analyses as well as extensive literature on A. muciniphila and intestinal homeostasis, we focused our functional experiments on this commensal bacterium. In our study we observed a decrease in the abundance of Blautia in NEMO Δhepa / Nlrp6 −/− mice as well. Various recent publications have demonstrated anti-inflammatory probiotic properties of Blautia species due to production of short-chain fatty acids 71 . It is likely that Blautia or other commensal strains might be protective as well. Future studies could explore the role of Blautia in the context of HCC development. Finally, microbiota modulation experiments using germ-free mice would provide even more experimental precision. In these studies, it would be interesting to study whether the transfer of Nlrp6 −/− microbiota will eventually result in enhanced HCC development. Our current bulk RNA sequencing data clearly links bacterial translocation to fibroinflammatory pathways as well as TF expression involved in T cell exhaustion. However, future studies including protein and histology data are warranted to substantiate these findings.
In summary, our data demonstrate that gut microbiota directly influence the hepatic inflammatory microenvironment in mice and men. An unfavorable microbiota-as seen in dysbiotic NEMO Δhepa /Nlrp6 −/− and transmissible to NEMO Δhepa micefuel liver disease progression by promoting mMDSCs and dampening CD8 + T cells. Importantly, microbiota modulation immediately reshapes the inflammatory microenvironment providing a rational for microbiota targeted therapies. The strong association of liver tissue microbiota and hepatic transcriptomic profile in cirrhosis patients calls for larger studies to assess its diagnostic application.

Methods
Mice. Male Alb-cre-NEMO Δhepa , Alb-cre-NEMO fl/fl referred to as WT, Alb-cre-NEMO Δhepa /Nlrp6 −/− and Alb-cre-NEMO Δhepa /Tlr4 −/− of the C57Bl6 background were bred and housed in the central animal facility of the University hospital RWTH Aachen. NEMO Δhepa /Nlrp6 −/− and NEMO Δhepa lines were generated from an initial heterozygous breeding and then separated for at least 3 generations to allow the development of the Nlrp6 −/− dysbiotic microbiota community 25 . Subsequently, these two lines were kept strictly separate and we did not allow any exchange of mice or bedding material between the two lines as the microbiota related phenotype of these mice has been shown to be transmissible upon cohousing 17 .
All mice were housed in the individually ventilated cages with access to a standard chow diet and drinking water ad libitum. Upon birth, male mice were assigned to either no treatment, FMT or ABx groups and followed up until week 13. Experiments for these age progression experiments were run and analyzed in parallel. FMT or ABx was initiated in the respective groups at 7-9 weeks of age and continued until week 13. All mice were housed at a temperature of 21−23°C with relative humidity of 35-65% and 12 h light/dark cycle. Cirrhosis cohort. Human cirrhosis liver tissue specimen were taken from patients that underwent liver transplantation between 1999 and 2005 at the University Hospital Bonn (Supplemental Table 2). The human ethics committee of the University of Bonn (029/13) approved the study. Healthy surgical tissue specimen were obtained from patients who underwent clinically indicated liver resection at University Hospital Bonn or University Hospital rechts der Isar of the Technical University Munich. All patients gave written informed consent to use excess biopsy material for research purposes. The study of these pseudonymized tissue specimen has been approved by the local ethics committee RWTH Aachen University (EK 196/19).
Depletion of microbiota with broad spectrum antibiotics. For microbiota depletion, a broad-spectrum antibiotic cocktail (ampicillin 1 g/l, vancomycin 1 g/l, gentamycin 160 mg/l, metronidazole 1 g/l) was administered in the drinking water of 8-week-old NEMO Δhepa /Nlrp6 −/− mice. To decrease the bitter taste of the antibiotics, 25 g glucose were added per liter. Antibiotic treatment was performed until week 13. Antibiotic water was replenished every second day.
Fecal microbiota transfer. For microbiota modulation experiments (fecal microbiota transfer, FMT), NEMO Δhepa mice were treated for 5 weeks three times/ week (Monday-Wednesday and Friday) via oral gavage with 200 µl of fecal dilution. To prepare this dilution, per mouse 20 mg of freshly harvested stool (immediately upon defecation) was collected from donor mice. Stool pellets were pooled and then vortexed for 5 min in 20 mg/100 µl anaerobic PBS to homogenize it almost entirely. Next, samples were gently centrifuged for 5 min at 350 × g to allow stool particulate to settle. The supernatant was collected and diluted 1:1 in anaerobic PBS. 200 µl of this suspension was transferred by oral gavage into recipient mice. This is Akkermansia muciniphila MucT strain was isolated in the lab of Willem de Vos 28,66 . It was grown as detailed by Depommier et al. Akkermansia muc. was stored in Glycerol at a concentration of 2 × 10 8 CFU/100 µl at −80°C. Immediately before gavage Akkermansia was thawed and diluted 1:2 in anaerobic PBS reduced with 0.5 g/l of L-cysteine-HCl. Mice were then gavaged with either 200 µl of this solution or anaerobic PBS.
Bone marrow transplantation. Bone marrow cells from WT and Tlr4 −/− donors were transplanted into 6-week-old WT, and NEMO Δhepa recipients after ablative γirradiation. Recipients were radiated twice with 6 Gy with an interval of 4 h. Donors were sacrificed and femur and tibia were exposed. With a fine needle the medullary canal was flushed with Hanks/FCS. After twice washing with Hanks/ FCS, cells were counted, and recipients received 1 × 10 6 cells via tail vein injection after the second radiation. During the first four weeks mice received antibiotic water to minimize the danger of infection. Mice were sacrificed 8 weeks after transplantation.
Intestinal permeability in vivo. Isothiocyanate conjugated dextran (FITC-dextran. molecular mass 4.0 kDa. Uppsala. Sweden) dissolved in PBS at a concentration of 200 mg/ml was administered to mice (10 ml/kg body weight) by oral gavage. 4 h after gavage the mice were sacrificed under general anesthesia by isoflurane. Blood samples were collected from inferior vena cava and immediately stored at 4°C in in the dark. Concentration of FITC in serum was determined by spectrophotofluorometry at an excitation wavelength of 485 nm (20 nm band width) and an emission wavelength of 528 nm (20 nm band width). Relative induction of FITC signal relative to age-matched WT control mice was calculated.
H&E-histology. Hematoxylin and eosin (H&E) staining was performed as previously described 18 . Briefly, tissue sections fixed in 4% paraformaledehyde (PFA) were cut into 2 µm sections. Tissue sections were deparaffinized and rehydrated. Next samples were stained with Mayer's Hematoxylin solution for 1 min. Samples were rinsed in tap water for 15 min, placed in distilled water for 30 s, placed in 95% alcohol for 30 s and next counterstained in Eosin solution for 1 min. Finally, samples were dehydrated and mounted with coverslips using the the Roti ® Histokit.
Sirius Red staining. Liver fibrosis development was studied using the following protocol. First, tissue sections embedded in paraffin were stained with Sirius red. For this purpose, tissue sections were deparaffinized by heating the slides at 65°C for 15 min, followed by 2 × 5 min in xylene, and rehydration by introducing a descending concentration of ethanol (100% ethanol and 96% ethanol, 5 min in 70% ethanol and distilled water). Tissue sections were then placed for 45 min in a 0.1% Sirius red solution, followed by 2 × 15 s incubation in 0.5% glacial acetic acid. Finally, sections were dehydrated by ascending alcohol incubations (2 min 96%, 2 × 5 min 100% ethanol and 2 × 5 min xylene). Mounting of Tissue sections was performed with coverslips using the Roti ® Histokit.
Immunohistochemistry staining. Five µm thick formalin-fixed, paraffinembedded liver tissue sections were used to perform immunohistochemical stainings. First, the tissue sections were deparaffinized and rehydrated. For Antigen recovery, sections were heated in a pressure cooker in citrate buffer (pH 6.0). The tissue sections were then immersed in H 2 O 2 solution (0.3% in methanol) for 10 min to block the endogenous peroxidases. To further block unspecific binding, the tissue sections were incubated in 1% bovine serum albumin in PBS for 2 h. Blocking was followed by incubation of the tissue sections overnight with the primary antibodies (Supplementary Table 6) at 4°C in a humid chamber. After primary antibody incubations tisue sections were washed thoroughly in PBS. Next, the tissue sections were incubated with appropriate horseradish peroxidaseconjugated secondary antibodies (Supplementary Table 6) in a humid chamber at room temperature. Visualized of target signals was achieved by staining with 3,3′diaminobenzidine solution (Vector Laboratories, Burlingame, CA, USA) for 2-5 min under the microscope. The nuclei were visualized by hematoxylin counterstaining. Finally, the stained sections were dehydrated in increasing concentrations of ethanol and mounted in Entellan.
Immunofluorescence staining. After collection tissue specimens were immediately embedded in Tissue-Tek. Using a cryotome, tissues were cut into 5 µm-thick sections and stored at −80°C. Slides were air-dried for 30 min at RT followed by 4% PFA fixation. Next, tissue samples were encircled using a hydrophobic pen and blocked with 5% goat serum for 1 h at RT in a humidity chamber.
After blocking, samples were incubated with the primary antibodies (Supplementary Table 6) at 4°C in a humidity chamber overnight. Samples were washed thoroughly in PBS and then incubated with the secondary antibodies (Supplementary Table 6) for 1 h in a humidity chamber. After incubation, sections were washed again thoroughly in PBS. Finally, sections were mounted in a DAPI (Vector Laboratories, Burlingame, CA, USA) aqueous medium to counterstain nuclei. Staining of mucus and gut bacteria was performed according to an established protocol 72 . Briefly, colon tissue sections containing feces were fixed using the Carnoy fixation method (60% absolute methanol, 30% chloroform, 10% glacial acetic acid). After paraffin embedding, mucus and gut bacteria were stained with an anti-Muc2 primary antibody and a fluorescence in situ hybridization (FISH) probe against eubacteria (16S rRNA: 5′-GCTGCCTCCCGTAGGAGT-3′).
Flow cytometry analysis of intrahepatic leukocytes. Same amounts of livers were digested by collagenase type IV for 1 h at 37°C (Worthington Biochemical Corporation, Lakewood, NJ, USA) and intrahepatic immune cells were isolated by multiple differential centrifugation steps as detailed 73  DNA Isolation and 16S rRNA amplicon sequencing. For 16 S rRNA gene sequencing, DNA was isolated from fecal samples using an established protocol 74 . Briefly, each sample (around 200 mg) was resuspended in 500 µl of extraction buffer (200 mM Tris, 20 mM EDTA, 200 mM NaCl, pH 8.0). 200 µl of 20% SDS. 500 µl of phenol:chloroform:isoamyl alcohol (24:24:1) and 100 µl of zirconia/silica beads (0.1 mm diameter). Samples were homogenized twice with a bead beater (BioSpec, Bartlesville, OK, USA) for 2 min. After precipitation of DNA, crude DNA extracts were resuspended in TE Buffer with 100 µg/ml RNase I and column purified to remove PCR inhibitors.
Amplification of the V4 region (F515/R806) of the 16S rRNA gene was performed according to previously described protocols 75 . Briefly, for 16S rRNA amplicon sequencing 25 ng of DNA were used per PCR reaction (30 µl). The PCR conditions consisted of initial denaturation for 30 s at 98°C, followed by 25 cycles (10 s at 98°C, 20 s at 55°C, and 20 s at 72°C. Each sample was amplified in triplicates and subsequently pooled. After normalization PCR amplicons were sequenced on an Illumina MiSeq platform (PE250).
16S rRNA analysis was conducted based on a previously described computational workflow 76 . In brief, obtained reads were assembled, quality controlled and clustered using Usearch8.1 (http://www.drive5.com/usearch/). Next, reads were merged using -fastq_mergepairs -with fastq_maxdiffs 30 and quality controlled with fastq_filter (-fastq_maxee 1), minimum read length 200 bp. The OTU and representative sequences were determined using the UPARSE algorithm 77 , followed by taxonomy assignment using a curated Silva database v128 78 and the RDP Classifier 79 with a bootstrap confidence cutoff of 80%. The OTU absolute abundance table and mapping file were used for statistical analyses and data visualization in the R statistical programming environment (http://www.rproject.org) package phyloseq 80 . The permutational multivariate ANOVA (ADONIS test) was performed in R. Factors with p value < 0.05 were considered as significant. Differential abundance analysis (DAA) was performed using a consensus approach based on multiple methods (DESeq2, LefSE, and ALDEx2) to help ensure robust biological interpretation 81 . DESeq2 was performed using the parameters, test = "Wald", fitType = "parametric", alpha = 0.01) 82 . OTUs were considered significantly DA between genotypes if their adjusted p-value was <0.05 and if the estimated 2-fold change was >2 (Love et al., 2014, McMurdie and Holmes, 2014). LefSe was performed using the R wrapper lefser (Khleborodova A 2021) with the following parameters kruskal.threshold = "0.05", wilcox.threshold = "0.05", lda.threshold = "2.5". ALDEX2 83 Was performed using default settings, OTUs were considered significantly DA between contrasts if (we.eBH Expected Benjamini-Hochberg corrected p value of Welch's t test) or (wi.eBH Expected Benjamini-Hochberg corrected p value of Wilcoxon test) was <0.05.
16S rDNA quantitation and taxonomic profiling in liver tissue. Microbial DNA was isolated from frozen liver biopsies with a protocol designed to minimize the risk of contamination between samples, by the environment or experimenters as previously described 32 . Negative controls consisting of molecular grade water were placed in separate isolation tubes during the isolation process and processed simultaneously throughout the protocol. DNA was amplified using real-time polymerase chain reaction (qPCR) amplification using universal 16S primers targeting the hypervariable V3-V4 region of the bacterial 16s ribosomal RNA gene. qPCR was performed on a ViiA 7 ® PCR system (Life Technologies, Carlsbad, CA, USA) using Sybr Green technology. Quality control and quantification of the extracted nucleic acids were performed based on gel electrophoresis (1% w/w agarose in TBE 0.5x) and absorption spectroscopy with a NanoDrop 2000 UV spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Highthroughput next-generation sequencing of microbial rDNA was performed using Illumina MiSeq technology as previously described 84 . Next, (a) The last 20 bases of reads R1 were removed; (b) the last 40 bases of reads R2 were removed; (c) amplicons <350 or >500 nucleotides in length were removed; (d) OTUs with a frequency <0.005% of the total record frequency have been removed; (e) Total Sum Scaling (TSS) normalization was used to normalize OTU read counts to relative frequencies. Because the number of sequences per sample was high and fairly constant between samples ( Supplementary Fig. 10a), we chose not to rarefy the data in order to normalize the number of sequences in each sample.
Numerous controls both in vitro and in silico were included to ensure the absence of artifacts related to non-specific amplification of eukaryotic DNA or reagent contamination 33 . Negative controls and liver samples were compared based on qPCR and beta diversity analyses and showed a clear separation ( Supplementary  Fig. 10b,c).
In line with our previous data, these numerous quality controls demonstrate that potential bacterial contamination was well contained and had a negligible impact on the taxonomic profiles of the samples in our study 33,85,86 .
qRT-PCR. Frozen tissue samples from liver or intestine were homogenized in 1 ml Trizol Reagent (Life Technologies, Carlsbad, CA, USA). 200 µl chloroform were added to separate the phases, the upper aqueous phase was transferred into a new collection tube. 500 µl isopropanol were added and the samples remained at RT for 15 min. Afterwards, the RNA was pelleted by centrifugation at 13,000 × g for 10 min at 4°C, the supernatant was discarded, and the pellets were washed twice with ethanol 70%. Next pellets were air dried and 300 µl DEPC water was used for resuspension. For transcription 1 µg of the isolated mRNA were used and reverse transcription into cDNA was performed using Omniscript ® RT Kit (Cat. No. 205113. Qiagen, Venlo, The Netherlands) according to the manufacturer's protocol. Real-time PCR reactions were performed with Real-Time PCR System Quant studio 6 Flex (Thermo Fisher Scientific, Waltham, MA, USA) and Fast SYBR ® GreenER Master Mix (Thermo Fisher Scientific, Waltham, MA, USA) according to manufacturer's recommendations. The primers were diluted 1:10 fold or 1:50 respectively. All primer sequences are listed (Supplemental Table 7). The Quant Studio Flex software (Thermo Fisher Scientific, Waltham, MA, USA) was used for analysis. In the following the relative mRNA expression was calculated with the 2 −ΔΔ CT method comparing target gene expression to the GAPDH housekeeping gene. mRNA sequencing analysis Pre-processing and normalization of RNA-seq data. FASTq files were aligned against the reference genome using the web application BioJupies. The count data were normalized using the Bioconductor package edgeR (version 3.30.0) that filters for lowly expressed genes and corrects for differences in library composition 87 . Using the Bioconductor package limma (version 3.44.1) we transformed the normalized data to log2-counts per million 88 .
Transcription factor activity inference with DoRothEA. Transcription factor (TF) activity can be inferred from gene expression data by interrogating the expression of the respective transcriptional targets (i.e., its regulon). It has been shown that this approach is more robust and accurate than observing the expression of the TF itself. We used DoRothEA as the regulon resource as it contains signed TF-target interactions for the majority of all human (and mouse) TFs 35 . Internally DoR-othEA uses the statistical method viper to access the TF activity from gene expression data and returns for each TF a normalized enrichment score (NES) that we consider a proxy for TF activity.
DoRothEA was applied to the normalized gene expression matrix with the following arguments: "method = 'scale'", "nes = T," "minsize = 4" and "eset.filter = F", using the Bioconductor package dorothea (version 1.0.0; https:// saezlab.github.io/dorothea/). Differences in TF activities between healthy and cirrhotic patients were computed with a t-test. To adjust p-values for multiple hypothesis testing we computed the false discovery rate (FDR).
Pathway activity inference with PROGENy. PROGENy is a tool that allows predicting pathway activities from gene expression data in human (and mouse) 34 . Instead of interrogating the expression of pathway members, PROGENy takes the expression of the most responsive genes of a pathway into account. These most responsive genes upon pathway perturbation are referred to as footprints (the concept of footprints is reviewed in ref. 89 . With PROGENy it is possible to infer the activity of these 14 signaling pathways in human (and mouse): Androgen, EGFR, Estrogen, Hypoxia, JAK-STAT, MAPK, NFkB, PI3K, TGFb, TNFa, Trail, p53, VEGF and WNT.
We applied PROGENy to the normalized gene expression matrix with the following parameters "top = 100", "perm = 1", "scale = T", using the Bioconductor package progeny (version 1.10.0; https://saezlab.github.io/progeny/). Differences in pathway activities between healthy and cirrhotic patients were computed with a t-test. To adjust p-values for multiple hypothesis testing we computed the false discovery rate (FDR).
As suggested by the xCell vignette we transformed the raw counts of the gene expression data to transcripts per million (TPM). Afterward, xCell was applied to the TPM matrix using the R package xCell (version 1.1.0; https://github.com/ dviraran/xCell).
Differences in cell type enrichment between healthy and cirrhotic patients were computed with a t-test. To adjust p-values for multiple hypothesis testing we computed the false discovery rate (FDR).
Immunoblotting. The liver and intestine tissue samples were homogenized with NP-40 Buffer containing phosphatase inhibitor cocktail tables (cOmplete mini, PhosSTOP (Roche, Basel, Switzerland) for protein isolation. Protein concentrations were measured using BIO-RAD protein reagent, then adapted to 2 µg/µl, before the proteins were separated electrophoretically on pre-cast 4-12% polyacrylamide gel (Bio-Rad, Hercules, CA, USA) in SDS running buffer at 160 V. After running, the gel was immediately placed in buffer to transfer the proteins to the nitrocellulose blotting membrane with the Trans-Blot Turbo Transfer System (Bio-Rad, Hercules, CA, USA). The success of transfer was checked using Ponceau Red. Before incubating with primary antibodies, the membrane was blocked with 5% non-fat dry milk or 5% BSA diluted in TBS-Tween (TBST 0.5%) to prevent unspecific antibody binding. Subsequently, the membrane was incubated with primary antibodies diluted 1:1000 in 5% dry milk or BSA overnight at 4°C under agitation. The horseradish peroxidase (HRP)-conjugated secondary antibodies were diluted 1:2000 in 5% dry milk and the membrane was incubated for 1 h at RT. ECL substrate (Pierce, Waltham, MA, USA) developing solution was used before image acquisition with the LAS mini 4000 developing machine (Fuji). Protein expression was quantitatively analyzed with ImageJ in relation to the expression of GAPDH. In-vitro MDSC assay MDSC isolation. MDSCS were isolated with Myeloid-Derived Suppressor Cell Isolation Kit (mouse; 130-094-538, Miltenyi, Wuppertal, Germany) from liver. After preparing a single cell suspension, the cell number was determined. Cell suspension was centrifuged at 300 × g for 10 min at 4°C. Supernatant was aspirated completely. Cell pellet was resuspended in 350 μl of buffer per 10 8 total cells and 50 µl of FcR Blocking Reagent per 10 8 total cells were added, mixed, and incubated for 10 min in the refrigerator (2−8°C). 100 μl of Anti-Ly-6G-Biotin (MDSC-Kit) were added, mixed, and incubated for 10 min in the refrigerator (2−8°C). Cells were washed by adding 5−10 ml of buffer per 10 8 cells and centrifuged at 300 × g for 10 min at 4°C. Supernatant was aspirated completely and up to 10 8 cells were resuspended in 800 μl of buffer. 200 μl of Anti-Biotin MicroBeads were added, mixed, and incubated for 15 min in the refrigerator (2−8°C). Cells were washed by adding 10−20 ml of buffer per 10 8 cells and centrifuged at 300 × g for 10 min at 4°C. Supernatant was aspirated completely and up to 10 8 cells were resuspended in 500 μl of buffer. LS Column was placed in the magnetic field of a suitable MACS Separator. Column was rinsed with 3 ml of buffer and cell suspension applied onto the column. Flow-through was collected which contained the unlabeled cells. Column was washed with 3 × 3 ml of buffer. The unlabeled cells which passed through were combined with the effluent from step 3; These cells represented the unlabeled pre-enriched Gr-1 dim Ly-6Gcell fraction. Column was removed from separator and a collection tube was placed under. 5 ml of buffer was added onto the column and the magnetically labeled cells were flushed out by firmly pushing the plunger into the column. These cells represented the labeled Gr-1 high Ly-6G + cell fraction.
The unlabeled pre-enriched Gr-1 dim Ly-6G − cell fraction was centrifuged at 300 × g for 10 min at 4°C. Supernatant was aspirated completely and up to 10 8 cells were resuspended in 400 µl buffer. 100 µl of Anti-Gr-1-Biotin per 10 8 cells was added, mixed, and incubated for 10 min at 4°C. Per 10 8 cells 5-10 ml of buffer were added and centrifuged at 300 × g for 10 min at 4°C. Supernatant was aspirated completely and up to 10 8 cells were resuspended in 900 μl of buffer. In addition, 100 µl of Streptavidin MicroBeads were added, mixed, and incubated for 15 min at 4°C. 10-20 ml buffer per 10 8 cells were added and centrifuged at 300 × g for 10 min at 4°C. Supernatant was aspirated completely and up to 10 8 cells were resuspended in 500 μl of buffer. MS columns were placed in the magnetic field and 500 µl of buffer were added onto the column. Cell suspension was applied onto the column and the collected and represented the unlabeled cells. The column was washed 3 × 500 µl. All flow through were collected. Column was removed from separator and a collection tube was placed under. 1 ml of buffer was added onto the column and the magnetically labeled cells were flushed out by firmly pushing the plunger into the column. These cells represented the labeled Gr-1 dim Ly-6G − cell fraction.
T cell isolation. T cells were isolated with (mouse; 130-095-130, Miltenyi, Wuppertal, Germany) from spleen. After preparing a single cell suspension, cell number was determined. Up to 10 7 cells were resuspended in 40 µl buffer and 10 µl of biotin-antibody cocktail per 10 7 total cells were added, mixed, and incubated for 5 min at 4°C. 30 µl of buffer and 20 µl of Anti-Biotin MicroBeads per 10 7 total cells were added, mixed, and incubated for 10 min at 4°C. LS columns were placed in the magnetic field and 3 ml of buffer added onto the column. Cell suspension was applied onto the column and flow through collected. Column was washed 3 × 3 ml and flow through collected.
T cell CFSE labeling. T cells were centrifuged with 300 × g for 10 min at 4°C and resuspended in 1 ml PBS/0.1% BSA. A solution of CFDA-SE (Vybrant CFDA SE Cell Tracer Kit, V12883, Thermo Fisher Scientific, Waltham, MA, USA) from DMSO Stock at 2X final labeling solution was prepared (100 µM). T cells were resuspended in 1 ml solution containing CFDA-SE dilution and incubated in the dark for 15 min at 37°C. Cells were quenched with 4 ml ice cold T cell medium and centrifuged with 300 × g for 10 min at 4°C. Cells were washed two times.
Proliferation was analyzed using a FACS Fortessa (BD, Bioscience, Heidelberg, Germany). Data were analyzed with the FlowJo software (Ashland, OR, USA).
Measurement of routine serum parameters. Routine serum parameters alanine aminotransferase (ALT), aspartate aminotransferase (AST), glutamate dehydrogenase (GLDH) and alkaline phosphatase (AP) were measured in the central laboratory of clinical chemistry in RWTH Aachen University Hospital.
Quantification and statistical analyses. For comparisons of two groups, significance was tested by unpaired two-tailed Student's t test. In case of more than two groups, we employed one-way ANOVA followed by Tukey-test with adjusted p-value for multiple comparisons. For not normally distributed data, two groups were compared using Wilcoxon-Mann-Whitney-Test and in case of more than two groups Kruskal-Wallis test with Dunn-Bonferroni-Test was used. Data were considered significant between experimental groups as: *p < 0.05. **p < 0.01 or ***p < 0.001.
Statistical analyses of 16S microbiota data was performed using R version 3.4.3 (2017-11-30) (http://www.rproject.org) and the packages 'phyloseq'. and 'ggplot2' 80,90 . The permutational multivariate analysis of variance test (ADONIS) and analysis of similarities (ANOSIM) were computed with 999 permutations. For ADONIS tests, a R 2 > 0.1 (effect size 10%) and p-value < 0.05 was considered as significant. RNA Sequencing data were analyzed using R as detailed above. The clinical cirrhosis cohort was analyzed using IBM SPSS Statistics software (Version 25). For graphic representation and statistical analysis R version 3.6, Rstudio and GraphPad Prism 8.0 were used.